Cancer Registry Information Extraction via Transfer Learning

Abstract

A cancer registry is a critical and massive database for which various types of domain knowledge are needed and whose maintenance requires labor-intensive data curation. In order to facilitate the curation process for building a high-quality and integrated cancer registry database, we compiled a cross-hospital corpus and applied neural network methods to develop a natural language processing system for extracting cancer registry variables buried in unstructured pathology reports. The performance of the developed networks was compared with various baselines using standard micro-precision, recall and Fmeasure. Furthermore, we conducted experiments to study the feasibility of applying transfer learning to rapidly * Corresponding authors develop a well-performing system for processing reports from different sources that might be presented in different writing styles and formats. The results demonstrate that the transfer learning method enables us to develop a satisfactory system for a new hospital with only a few annotations and suggest more opportunities to reduce the burden of cancer registry curation.

Integrating predictive coding and a user-centric interface for enhanced auditing and quality in cancer registry data

“…To create a lung cancer registry concept recognition corpus, we followed the similar annotation procedure suggested in our previous work [19] for colorectal cancer. Referencing Table 3 , we delineated 26 cancer registry-related concepts within the annotation guidelines, instructing annotators to mark these concepts if mentioned in the provided reports.…”

LPAdaIN: Light Progressive Attention Adaptive Instance Normalization Model for Style Transfer

“…To achieve arbitrary style transfer, Huang et al [10] proposed an adaptive instance normalization (AdaIN) layer which aligned the mean and variance of the content feature maps to those of the style feature maps to produce the stylized feature maps. Without training a specific style transfer network, Li et al [11] proposed whitening and coloring transforms (WCT), which were integrated in the feed-forward procedure to match the statistical distributions and correlations between the intermediate features of content and style. Arbitrary style transfer models in feed-forward style transfer methods are more efficient and do not require online training.…”